Using Species Distribution Models For Fungi

Species distribution models (SDMs) are an emerging tool in the study of fungi, and their use is expanding across species and research topics. To summarise progress to date and to highlight important considerations for future users, we review 283 studies that apply SDMs to fungi. We found that macrofungi, lichens, and pathogenic microfungi are most often studied. While many studies only aim to model species response to environmental covariates, the use of SDMs for explicitly predicting fungal occurrence in space and time is growing. Many studies collect fungal occurrence data, but the use of pre-collected records from reference collections and citizen science programs is increasing. Challenges of applying SDMs to fungi include detection and sampling biases, and uncertainties in identification and taxonomy. Further, finding environmental covariates at appropriate spatial and temporal scales is important, as fungi can respond to fine-scale environmental patterns. Fine-scale covariate data can be difficult to gather across space, but we show remote-sensing measurements are viable for fungi SDMs. For those fungi interacting with host species, host information is also important, and can be used as covariates in SDMs. We also highlight that competition among fungi, and dispersal, can affect observed distributions, with the latter particularly prominent for invasive fungi. We show how one can account for these processes in models, when suitable data are available. Finally, we note that environmental DNA records create new opportunities and challenges for future modelling efforts, and discuss the difficulties in predicting invasions and climate change impacts. The application of SDMs to fungi has already provided interesting lessons on how to adapt modelling tools for specific questions, and fungi will continue to be relevant test subjects for further technical development of SDMs.     

The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species

Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift. Species Distribution Models (SDMs) can help forecast such range shift responses. For few species, both correlative and mechanistic SDMs were built, but allis shad (Alosa alosa), an endangered anadromous fish species, is one of them. The main purpose of this study was to provide a framework for joint analyses of correlative and mechanistic SDMs projections in order to strengthen conservation measures for species of conservation concern. Guidelines for joint representation and subsequent interpretation of models outputs were defined and applied. The present joint analysis was based on the novel mechanistic model GR3D (Global Repositioning Dynamics of Diadromous fish Distribution) which was parameterized on allis shad and then used to predict its future distribution along the European Atlantic coast under different climate change scenarios (RCP 4.5 and RCP 8.5). We then used a correlative SDM for this species to forecast its distribution across the same geographic area and under the same climate change scenarios. First, projections from correlative and mechanistic models provided congruent trends in probability of habitat suitability and population dynamics. This agreement was preferentially interpreted as referring to the species vulnerability to climate change. Climate change could not be accordingly listed as a major threat for allis shad. The congruence in predicted range limits between SDMs projections was the next point of interest. The difference, when noticed, required to deepen our understanding of the niche modelled by each approach. In this respect, the relative position of the northern range limit between the two methods strongly suggested here that a key biological process related to intraspecific variability was potentially lacking in the mechanistic SDM. Based on our knowledge, we hypothesized that local adaptations to cold temperatures deserved more attention in terms of modelling, but further in conservation planning as well.     

Using Landscape and Bioclimatic Features to Predict the Distribution of Lions,Leopards and Spotted Hyaenas in Tanzania's Ruaha Landscape

Tanzania''s Ruaha landscape is an international priority area for large carnivores, supporting over 10% of the world''s lions and important populations of leopards and spotted hyaenas. However, lack of ecological data on large carnivore distribution and habitat use hinders the development of effective carnivore conservation strategies in this critical landscape. Therefore, the study aimed to (i) identify the most significant ecogeographical variables influencing the potential distribution of lions, leopards and spotted hyaenas across the Ruaha landscape; (ii) identify zones with highest suitability for harbouring those species; and (iii) use species distribution modelling algorithms (SDMs) to define important areas for conservation of large carnivores. Habitat suitability was calculated based on environmental features from georeferenced presence-only carnivore location data. Potential distribution of large carnivores appeared to be strongly influenced by water availability; highly suitable areas were situated close to rivers and experienced above average annual precipitation. Net primary productivity and tree cover also exerted some influence on habitat suitability. All three species showed relatively narrow niche breadth and low tolerance to changes in habitat characteristics. From 21,050 km² assessed, 8.1% (1,702 km²) emerged as highly suitable for all three large carnivores collectively. Of that area, 95.4% (1,624 km²) was located within 30 km of the Park-village border, raising concerns about human-carnivore conflict. This was of particular concern for spotted hyaenas, as they were located significantly closer to the Park boundary than lions and leopards. This study provides the first map of potential carnivore distribution across the globally important Ruaha landscape, and demonstrates that SDMs can be effective for understanding large carnivore habitat requirements in poorly sampled areas. This approach could have relevance for many other important wildlife areas that only have limited, haphazard presence-only data, but which urgently require strategic conservation planning.     

Using Genome-Wide Protein Sequence Data to Predict Amino Acid Conservation

For most proteins, multiple sequence alignments are a viable method to identify functionally and structurally important amino acids, but for most organisms, there is a subset of proteins that are unique or found in a few closely related organisms. For these proteins, it is not possible to produce sequence alignments that are useful in identifying functionally or structurally important amino acids. We have investigated the relationship between amino acid conservation and five factors (the amino acid’s identity, N-terminal neighbor, C-terminal neighbor, the local hydropathy of surrounding amino acids, and the local expected net charge of the surrounding amino acids based on the primary sequence) in Escherichia coli proteins. For four of the factors examined (all but the amino acid’s identity), there is a significant relationship with conservation for some of the standard 20 amino acids. Using the combination of all five factors, we show that it is possible to calculate a score based on the primary sequences of a subset of E. coli proteins that has statistically significant predictive value with respect to predicting conserved amino acids in other E. coli proteins and Saccharomyces cerevisiae proteins. As these five variables show significant relationships with conservation, we have termed them conservation factors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Forest Landscape Restoration Contributes to the Conservation of Primates in the Gishwati-Mukura Landscape,Rwanda

International Journal of Primatology - Land-use change is the main driver of habitat loss and fragmentation for primates, resulting in declines in species diversity and population size. The...     

Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.     

Effects of Landscape Elements on the Distribution of the Rare Bumblebee Species Bombus muscorum in an Agricultural Landscape

The regional distribution pattern of Bombus muscorum was studied in an agricultural landscape of central Germany, one of two remaining areas with the occurrence of this nationally endangered species in the Land Hesse. To determine the landscape characteristics that facilitate the occurrence of B. muscorum, grid-based observation records were analysed in a GIS environment at a regional scale. A significantly negative effect of the number of trees on the occurrence of B. muscorum and a significantly positive one of the proportion of arable land, strongly support the species’ preference for open landscapes. Yet, apart from open landscapes additional landscape features were shown to be important. A significantly positive effect of ditches in the final model revealed the importance of this landscape element for the occurrence of B. muscorum. This finding was additionally supported by recordings of nest-searching queens, nests, and flower visits along dithes. The positive effects of clover and fallow land indicate the species’ need for suitable food resources throughout the season. Because B. muscorum exhibits small foraging ranges, it is essential that landscape elements that provide nesting sites, foraging habitats and undisturbed hibernation structures are next to each other. The low numbers of individuals of B. muscorum recorded indicate that the supply of these habitat elements may have reached a critical threshold in the study region.     

Species Distribution Models of Tropical Deep-Sea Snappers

Deep-sea fisheries provide an important source of protein to Pacific Island countries and territories that are highly dependent on fish for food security. However, spatial management of these deep-sea habitats is hindered by insufficient data. We developed species distribution models using spatially limited presence data for the main harvested species in the Western Central Pacific Ocean. We used bathymetric and water temperature data to develop presence-only species distribution models for the commercially exploited deep-sea snappers Etelis Cuvier 1828, Pristipomoides Valenciennes 1830, and Aphareus Cuvier 1830. We evaluated the performance of four different algorithms (CTA, GLM, MARS, and MAXENT) within the BIOMOD framework to obtain an ensemble of predicted distributions. We projected these predictions across the Western Central Pacific Ocean to produce maps of potential deep-sea snapper distributions in 32 countries and territories. Depth was consistently the best predictor of presence for all species groups across all models. Bathymetric slope was consistently the poorest predictor. Temperature at depth was a good predictor of presence for GLM only. Model precision was highest for MAXENT and CTA. There were strong regional patterns in predicted distribution of suitable habitat, with the largest areas of suitable habitat (> 35% of the Exclusive Economic Zone) predicted in seven South Pacific countries and territories (Fiji, Matthew & Hunter, Nauru, New Caledonia, Tonga, Vanuatu and Wallis & Futuna). Predicted habitat also varied among species, with the proportion of predicted habitat highest for Aphareus and lowest for Etelis. Despite data paucity, the relationship between deep-sea snapper presence and their environments was sufficiently strong to predict their distribution across a large area of the Pacific Ocean. Our results therefore provide a strong baseline for designing monitoring programs that balance resource exploitation and conservation planning, and for predicting future distributions of deep-sea snappers.     

Using California Gnatcatcher to Test Underlying Models in Habitat Conservation Plans

Abstract: Habitat Conservation Plans are a widely used strategy to balance development and preservation of species of concern and have been used in southern California, USA, to protect the coastal California gnatcatcher (Polioptila californica). Few data exist on gnatcatcher abundance and distribution, and existing data have problems with issues of closure (i.e., sampling occurs in a short enough time period such that abundance or distribution are not changing), detectability, and proper attention to probability-based sampling schemes. Thus, a habitat model has been relied upon in reserve design. California gnatcatchers are the flagship and umbrella species of many plans and we provide the first estimates that incorporate probabilistic sampling and test predictions from the habitat model. Probability of occurrence was 26% (SĚ = 0.06); however, occupancy varied by modeled habitat quality with slopes <40%, warm, and wet sagebrush habitat having higher occupancy probabilities. Interpreting abundance and occupancy probabilities by vegetation type was complicated by error detected in Geographic Information System vegetation metadata files. The slope (1.08, SĚ = 0.66), temperature (0.79, SĚ = 0.70), and precipitation (—2.62, SĚ = 1.21) variables associated with habitat models were stronger influences on occupancy than was patch size (0.48, SĚ = 0.66). Previous models weight patch size equal to slope and climate. Our work demonstrates that probabilistic sampling can be carried out on a large scale and the results provide better information for managers to make decisions about their reserves.     

Integrating Multiple Distribution Models to Guide Conservation Efforts of an Endangered Toad

Species distribution models are used for numerous purposes such as predicting changes in species’ ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad (Anaxyrus californicus), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species’ current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.     

Models of Experimentally Derived Competitive Effects Predict Biogeographical Differences in the Abundance of Invasive and Native Plant Species

Mono-dominance by invasive species provides opportunities to explore determinants of plant distributions and abundance; however, linking mechanistic results from small scale experiments to patterns in nature is difficult. We used experimentally derived competitive effects of an invader in North America, Acroptilon repens, on species with which it co-occurs in its native range of Uzbekistan and on species with which it occurs in its non-native ranges in North America, in individual-based models. We found that competitive effects yielded relative abundances of Acroptilon and other species in models that were qualitatively similar to those observed in the field in the two ranges. In its non-native range, Acroptilon can occur in nearly pure monocultures at local scales, whereas such nearly pure stands of Acroptilon appear to be much less common in its native range. Experimentally derived competitive effects of Acroptilon on other species predicted Acroptilon to be 4–9 times more proportionally abundant than natives in the North American models, but proportionally equal to or less than the abundance of natives in the Eurasian models. Our results suggest a novel way to integrate complex combinations of interactions simultaneously, and that biogeographical differences in the competitive effects of an invader correspond well with biogeographical differences in abundance and impact.     

Use of Phytosociological Databases for Species Distribution Models

Species Distribution Models are key in modern ecological studies. They employ information about species locations and environmental factors to generate statistical functions that predict the potential distribution of species on the basis of landscape suitability. Although these models are powerful and useful tools, often the required information about species distribution is lacking, and the only resources are pre-collected museum data. Phytosociological databases contain a myriad of relevés with precious information, but are often considered to be the exclusive ownership of vegetation scientists. Our study tested the efficiency of a phytosociological database in the building of Species Distribution Models, including spatial autocorrelation (SAC) as a predictor to evaluate its effects on model performance. Spatial autocorrelation (SAC) is a natural characteristic of species distribution that depends on exogenous and endogenous processes. The latter??s effects could be overestimated by a subjective sample choice. We chose Festuca riccerii, an Italian endemic species. We split the whole dataset (671 relevés) into a calibration (443 relevés) and testing set (228 relevés) and performed a GLM on these data to identify the main ecological factors that lead distribution in order to build a Species Distribution Model. The dataset??s efficiency was assessed by testing the predicting power of the calibrated model on the testing subset. The phytosociological database proved to be good for building model (AUC?=?0.821), providing a useful basis for fast and low cost ecological analysis, and could be used subsequently for more detailed analyses.     

Effects of Species Richness on Resident and Target Species Components in a Prairie Restoration

The ecological role of biodiversity in achieving successful restoration has been little explored in restoration ecology. We tested the prediction that we are more likely to create persistent, species‐rich plant communities by increasing the number of species sown, and, to some degree, by varying functional group representation, in experimental prairie plantings. There were 12 treatments consisting of 1‐, 2‐, 3‐, 4‐, 8‐, 12‐, and 16‐species mixtures of native perennials representing four functional groups (C₄ grasses, C₃ grasses, nitrogen‐fixing species, and late‐flowering composites) that predominate within Central Plains tallgrass prairies. In 2000, species were seeded into square plots (6 × 6 m), with five replicates per treatment, on former agricultural land. Annually, we measured total species richness and evenness, target species richness and cover, and richness and cover of resident species (i.e., those emerging from the seed bank). Both target species richness and rate of establishment of target communities were highest in the most species‐rich mixtures, but there was no additional benefit for treatments that contained more than eight species. Richness of resident species did not vary with target species richness; however, cover by resident species was lower in the higher target species treatments. Our results, indicating that establishment of species‐rich prairie mimics can be enhanced by starting with larger numbers of species at the outset, have implications for grassland restoration in which community biodiversity creation and maintenance are key goals.     

Contrasting Cloud Forest Restoration Potential Between Plantations of Different Exotic Tree Species

The role of exotic tree plantations for biodiversity conservation is contested. Such plantations nevertheless offer various ecosystem service benefits, which include carbon storage and facilitation of indigenous tree species regeneration. To assess forest restoration potential in tropical exotic tree plantations, we assessed native cloud forest tree regeneration in 166 plots in ca. 50‐year‐old plantations of five timber species that are widely used in tropical plantations (Pinus patula, Eucalyptus saligna, Cupressus lusitanica, Grevillea robusta and Acacia mearnsii). Differences in species abundance, diversity and composition were compared among plantations, and between plantations and disturbed and undisturbed indigenous Afromontane cloud forest (southeast Kenya) relicts after controlling for environmental variation between plots (i.e. altitude, distance to indigenous forest, soil depth, slope, aspect) and for environmental and stand structural variation (i.e. dominant tree height and basal area). Regenerating trees were mostly early‐successional species. Indigenous tree species regeneration was significantly higher in Grevillea plantations, where the seedling community also included late‐successional tree species. Regeneration under Eucalyptus was particularly poor. Acacia had a strong invasive nature, reducing its potential role and usefulness in indigenous forest restoration. Our study underlined that exotic tree plantations have differential effects on native tree species regeneration, with high potential for Grevillea plantations and low potential for invasive exotic species.     

Exploiting the Adaptation Dynamics to Predict the Distribution of Beneficial Fitness Effects

Adaptation of asexual populations is driven by beneficial mutations and therefore the dynamics of this process, besides other factors, depends on the distribution of beneficial fitness effects. It is known that on uncorrelated fitness landscapes, this distribution can only be of three types: truncated, exponential and power law. We performed extensive stochastic simulations to study the adaptation dynamics on rugged fitness landscapes, and identified two quantities that can be used to distinguish the underlying distribution of beneficial fitness effects. The first quantity studied here is the fitness difference between successive mutations that spread in the population, which is found to decrease in the case of truncated distributions, remains nearly a constant for exponentially decaying distributions and increases when the fitness distribution decays as a power law. The second quantity of interest, namely, the rate of change of fitness with time also shows quantitatively different behaviour for different beneficial fitness distributions. The patterns displayed by the two aforementioned quantities are found to hold good for both low and high mutation rates. We discuss how these patterns can be exploited to determine the distribution of beneficial fitness effects in microbial experiments.     

Quantification of Restoration Success Using Complex Systems Concepts and Models

Concepts and models from complex systems theory are introduced to help expand the approaches to quantify restoration success in ecology. Main points are illustrated using case studies. These include (1) recovery and restoration trajectories may be complex (nonlinear, unpredictable, and leading to multiple attractors), and thus knowledge of well‐known model trajectories from complex systems theory may be useful; (2) modeling may be inevitable, as complete and long‐term observation of recovery pathways are rarely possible; (3) holistic views (e.g., community level as opposed to population‐level) may be necessary to understand governing processes in restoration and recovery.